The skill of ECMWF cloudiness forecasts
Haiden, Thomas ; Forbes, Richard ; Ahlgrimm, Maike ; Bozzo, Alessio
Correctly predicting cloudiness is an important part of a successful weather forecast. Cloud cover is not just of interest in its own right but also has a major impact on other parameters, such as temperature and solar radiation. It is, however, often highly variable in terms of time and location and can therefore be difficult to forecast. The skill of ECMWF cloudiness forecasts improves if time-averaged values and ensemble forecasts are used, but some regions of the world pose particular challenges. We investigate cloud forecast skill in the ECMWF model by verifying total cloud cover and solar radiation forecasts against surface observations and satellite data, by analysing the scaledependence of skill, and by evaluating both high-resolution and ensemble forecasts of cloudiness and solar radiation.
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